DEVELOPMENT OF CHEMINFORMATIC MODULE FOR INFORMATION CONTENT ANALYSIS SOFTWARE

描述（由申请人提供）：在x射线晶体学和核磁共振技术进步的帮助下，基于靶标的药物发现的出现使制药公司遭受了信息过多和信息不足的困扰：信息过多的意义在于，早期对靶标发现的推动使制药公司的研发淹没在潜在的相关靶标和候选药物中；从某种意义上说，信息太少，即使是最先进的化合物筛选方法也无法产生目标与化合物之间的关系和趋势数据，这些数据可以帮助选择有希望的未来候选人。目前，一种候选化合物从成功到成功的过程需要数年时间，耗资数千万美元。早期的药物研发需要一种方法来简化这一阶段的开发，一种数学上和生物学上合理的方法来优化和排序候选化合物及其与有希望的生物靶点的关系。VaSSA技术是由Bioinformatica有限责任公司的Jeffrey Clark博士开发，并在密苏里大学Gerald Wyckoff博士的协助下完成的，它有可能解决这个问题。VaSSA是一种全新的生物数据处理方法。它测量信息内容，这是一个独立变量，允许对核苷酸和氨基酸数据进行严格的统计分析。事实证明，VaSSA仅通过信息内容分析就能成功地优化和排名生物学相关靶标。目前，它能够测量核苷酸和氨基酸数据中的信息含量；然而，我们相信，现有的语法检查肽数据可以修改，以测量有机化合物的信息含量。因此，本研究的目标是为VaSSA软件开发一个化学信息学模块，该模块将在药物发现周期中发挥关键作用。具体来说，化学信息学分析，特别是应用于目标-化合物关系和数据趋势的分析，可以极大地提高候选药物的识别能力，缩短从成功到成功的周期。该项目的具体目标是：(1)为VaSSA开发化学信息学模块；(2)使用现有的VaSSA肽语法验证化学信息学语法；(3)根据信息含量分析对250个有机分子进行排序并验证结果；(4)建立行业合作伙伴关系，以协助进一步开发该技术。Wyckoff博士与编程顾问合作，将完成模块构建和验证，并协助Bioinformatica LLC确保适当的行业合作伙伴，并就模块的未来应用提供建议，可能包括其在无机模块中的应用及其在药物发现周期的领先优化阶段的作用。

DESCRIPTION (provided by applicant): The advent of target-based drug discovery, assisted by advances in x-ray crystallography and NMR, has left pharmaceutical companies suffering both from too much and too little information: too much information in the sense that the early push for target discovery has left pharma's R&D drowning in potentially relevant targets and drug candidates; too little information in the sense that even cutting-edge in silico methods of compound screening fail to produce target-to-compound relational and trending data that could assist in the selection of promising future candidates. At present, the process of taking a candidate compound from a hit to a lead takes years and costs tens of millions of dollars. Early pharmaceutical R&D is in need of way to streamline this phase of development, a mathematically and biologically sound method for optimizing and ranking candidate compounds and their relationships to promising biological targets. The VaSSA technology, developed by Dr. Jeffrey Clark of Bioinformatica, LLC, and completed with the assistance of Dr. Gerald Wyckoff of the University of Missouri, has the potential to solve this problem. VaSSA is the implementation of an entirely novel approach to biological data. It measures information content, an independent variable that permits rigorous statistical analysis of nucleotide and amino acid data. VaSSA has proven successful at optimizing and ranking biologically relevant targets through information content analysis alone. At present, it is able to measure information content in nucleotide and amino acid data; however, we believe that an existing syntax for examining peptide data can be modified to measure the information content of organic chemical compounds. The goal of this study, therefore, is to develop a cheminformatic module for the VaSSA software that will play a critical role in the drug discovery cycle. Specifically, cheminformatic analysis, particularly as applied to target-to-compound relationships and the trends in that data, could vastly improve candidate identification and shorten the hits-to-leads cycle. The project's specific aims are to: (1) develop a cheminformatic module for VaSSA; (2) validate the cheminformatic syntax using existing VaSSA peptide syntax; (3) rank a set of 250 organic molecules based on information content analysis and validate results; and (4) develop an industry partnership to assist in further development of the technology. Dr. Wyckoff, in partnership with a programming consultant, will complete the module build and validation, as well as assisting Bioinformatica LLC in securing an appropriate industry partner and advising on future applications for the module, potentially including its application to inorganic modules and its role in the lead optimization phase of the drug discovery cycle. PUBLIC HEALTH RELEVANCE: We propose the creation of a cheminformatic module as an extension to the VaSSA software that is already in production. This software is meant to extend the range of services that Bioinformatica can provide by allowing for the integration of information theory in to the drug development cycle.